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This review article examines some advancements in electrochemical sensors for vitamin detection in the past few decades. Vitamins are micronutrients found in natural foods essential for maintaining good health. Most vitamins cannot be synthesized by a body and must be obtained externally from natural food. Vitamins make a class of organic chemicals that shortage can cause various ailments and diseases, and consumption can become harmful if it exceeds the usually needed level. Because of these factors, vitamin detection has become highly significant and sparked interest over the past few decades. The electrochemical sensors function on the concept of electro­chemical activity of practically all vitamins. This implies that concentrations of vitamins in the electrolyte may be detected by measuring the amounts of current generated at certain potentials by their oxidation and reduction at the working electrode surface. Voltammetric methods are superior to other methods because they are cheaper and show sharp sensitivity with faster analysis speed. The carbon-based electrodes, in particular carbon paste electrodes (CPE), have significant advantages like easier catalyst incorpo­ration, surface renewability, and expanded potential windows with lower ohmic resistance. This review goes into detail about several electro­chemical sensors involving CPE as the working electrode and its utilization to detect water- and fat-soluble vitamins.

Wild-type transthyretin amyloidosis (ATTRwt) is an underdiagnosed and potentially fatal disease. Interestingly, ATTRwt deposits have been found to deposit in the ligamentum flavum (LF) of patients with lumbar spinal stenosis before the development of systemic and cardiac amyloidosis. In order to study this phenomenon and its possible relationship with LF thickening and systemic amyloidosis, a precise method of quantifying amyloid deposits in histological slides of LF is critical. However, such a method is currently unavailable. Here, we present a machine learning quantification method with Trainable Weka Segmentation (TWS) to assess amyloid deposition in histological slides of LF. Images of ligamentum flavum specimens stained with Congo red are obtained from spinal stenosis patients undergoing laminectomies and confirmed to be positive for ATTRwt. Amyloid deposits in these specimens are classified and quantified by TWS through training the algorithm via user-directed annotations on images of LF. TWS can also be automated through exposure to a set of training images with user-directed annotations, and then applied] to a set of new images without additional annotations. Additional methods of color thresholding and manual segmentation are also used on these images for comparison to TWS. We develop the use of TWS in images of LF and demonstrate its potential for automated quantification. TWS is strongly correlated with manual segmentation in the training set of images with user-directed annotations (R = 0.98; p = 0.0033) as well as in the application set of images where TWS was automated (R = 0.94; p = 0.016). Color thresholding was weakly correlated with manual segmentation in the training set of images (R = 0.78; p = 0.12) and in the application set of images (R = 0.65; p = 0.23). TWS machine learning closely correlates with the gold-standard comparator of manual segmentation and outperforms the color thresholding method. This novel machine learning method to quantify amyloid deposition in histological slides of ligamentum flavum is a precise, objective, accessible, high throughput, and powerful tool that will hopefully pave the way towards future research and clinical applications.

OBJECTIVES/GOALS: Wild-type transthyretin amyloid (ATTRwt) deposits have been found to deposit in the ligamentum flavum (LF) of spinal stenosis patients prior to systemic and cardiac amyloidosis, and is implicated in LF hypertrophy. Currently, no precise method of quantifying amyloid deposits exists. Here, we present our machine learning quantification method. METHODS/STUDY POPULATION: Images of ligamentum flavum specimens stained with Congo red are obtained from spinal stenosis patients undergoing laminectomies and confirmed to be positive for ATTRwt. Amyloid deposits in these specimens are classified and quantified by TWS through training the algorithm via user-directed annotations on images of LF. TWS can also be automated through exposure to a set of training images with user- directed annotations, and then application to a set of new images without additional annotations. Additional methods of color thresholding and manual segmentation are also used on these images for comparison to TWS. RESULTS/ANTICIPATED RESULTS: We develop the use of TWS in images of LF and demonstrate its potential for automated quantification. TWS is strongly correlated with manual segmentation in the training set of images with user-directed annotations (R = 0.98; p = 0.0033) as well as in the application set of images where TWS was automated (R = 0.94; p = 0.016). Color thresholding was weakly correlated with manual segmentation in the training set of images (R = 0.78; p = 0.12) and in the application set of images (R = 0.65; p = 0.23). DISCUSSION/SIGNIFICANCE: Our machine learning method correlates with the gold standard comparator of manual segmentation and outperforms color thresholding. This novel machine learning quantification method is a precise, objective, accessible, high throughput, and powerful tool that will hopefully pave the way towards future research and clinical applications.

Advancements in the computing realm have assisted the Architecture, Engineering, and Construction (AEC) industry to progress significantly by automating several design tasks and activities. Building Information Modelling (BIM) authoring tools have played a significant role in automating design tasks and reducing the efforts required by the designer in redundant, repetitive or production-oriented activities. This paper explores one such approach that, with the help of BIM authoring tool and its Application Programming Interface (API), reduces the efforts expended on formwork design for concrete structures. The paper utilises the concept of using BIM data as input to compute the quantity of formwork, and generate visualisations and schedule of formwork. The developed approach first takes data input from semantic BIM to the API environment for computation and design of formwork systems, which is then placed within the BIM model, to generate visualisation and prepare schedules. The research work utilises a structural concrete wall as an example to demonstrate the presented approach. The approach will be influential in streamlining the formwork design process in the BIM environment and reducing efforts required by the designer and the planning engineer. Since the formwork elements are generated as 3-Dimensional (3D) solids and smart BIM elements, the generated model of formwork can be used for resolving clashes, scheduling, and resource planning.